This invention relates to a control method for deploying inflatable restraints in a vehicle crash event, and more particularly to a control method that utilizes fuzzy logic to determine the severity of the crash.
Vehicle inflatable restraint systems include one or more acceleration sensors, one or more restraint devices such as frontal or side air bags, and a signal processor for analyzing the acceleration signals and initiating deployment of the restraints if a detected crash is deemed to be sufficiently severe. In general, the acceleration signals are monitored to detect a potential crash event, and then integrated over the course of the detected event to produce a velocity change signal, which in turn can be used to gauge the crash severity; see, for example, U.S. Pat. No. 5,969,599 to Wessels et al. Frequently, numerous higher-order terms such as jerk or oscillation are also utilized to detect certain crash signal characteristics for either enabling or disabling deployment. When the severity measure crosses a deployment threshold (which may be fixed or variable), the restraints are deployed. If the restraints have multiple independently fired stages, multiple deployment thresholds may be used for determining which stages should be deployed; see, for example, U.S. Pat. No. 5,411,289 to Smith et al. Finally, another factor that is sometimes used in connection with restraint deployment involves predicting occupant movement due to the crash based on the measured acceleration signal; see, for example, U.S. Pat. No. 4,985,835 to Sterler et al. and 5,430,649 to Cashler et al.
Unfortunately, the above-described deployment controls frequently do not bear a physically meaningful relationship to the crash data, and therefore tend to require a high degree of calibration effort, particularly in establishing the deployment thresholds. For this and other reasons, it has been proposed to utilize fuzzy logic control principles to control restraint deployment; see, for example, U.S. Pat. No. 5,673,365 to Basehore et al. In this approach, a small number of variables having relevance to the deployment decision are characterized in terms of fuzzy membership functions, and then logically combined based on a number of physically meaningful fuzzy rules and consolidated (de-fuzzified) to form a deploy/no-deploy decision. However, the combination and consolidation of various rules inherent in the fuzzy inference control complicates the process of calibrating the system to produce the correct deployment decision in response to a given set of input conditions. Accordingly, what is needed is a fuzzy logic deployment control having a more direct and user-friendly calibration process.
The present invention is directed to an improved fuzzy logic control for controlling the deployment of inflatable restraints in a vehicle in response to measured acceleration of the vehicle during a crash event, wherein the fuzzy logic control determines the crash severity, and a deployment control algorithm uses the determined crash severity to control deployment of individual stages of the restraints. In a preferred implementation, the crash severity is not determined until the measured acceleration and the corresponding change in velocity exceed respective thresholds, and a prediction of occupant movement due to the measured acceleration exceeds a threshold. Once the respective thresholds are exceeded, the fuzzy logic control is initiated to determine the crash severity, and the deployment control algorithm determines whether to deploy individual stages of the restraints based on the determined crash severity and the elapsed time of the crash event.